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CLC number: TP391

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Received: 2008-04-30

Revision Accepted: 2008-08-15

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.10 P.1382-1389


A model for automatic identification of human pulse signals

Author(s):  Hui-yan WANG, Pei-yong ZHANG

Affiliation(s):  College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China; more

Corresponding email(s):   zhangpy@vlsi.zju.edu.cn

Key Words:  Pulse signal identification, Feature extraction, Bayesian network, Quantitative diagnosis, Wavelet transform

Hui-yan WANG, Pei-yong ZHANG. A model for automatic identification of human pulse signals[J]. Journal of Zhejiang University Science A, 2008, 9(10): 1382-1389.

@article{title="A model for automatic identification of human pulse signals",
author="Hui-yan WANG, Pei-yong ZHANG",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A model for automatic identification of human pulse signals
%A Hui-yan WANG
%A Pei-yong ZHANG
%J Journal of Zhejiang University SCIENCE A
%V 9
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%P 1382-1389
%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820332

T1 - A model for automatic identification of human pulse signals
A1 - Hui-yan WANG
A1 - Pei-yong ZHANG
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 10
SP - 1382
EP - 1389
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820332

This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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